Machine learning and essentialism
Main Article Content
Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Aristotle, 2014. Categories. In: Jonathan Barnes, ed. The Complete Works of Aristotle: The Revised Oxford Translation, One-Volume Digital Edition. 6. print., with corr. Vol. 71:2, Bollingen series. Princeton, N.J: Princeton University Press, pp.25–70.
Ben-Menahem, Y., 2006. Conventionalism : From Poincare to Quine [Online]. Cambridge; New York: Cambridge University Press. Available at: <https://search.ebscohost.com/login.aspx?direct=true&db=e000xww&AN=529339&lang=pl&site=ehost-live> [visited on 13 January 2023].
Bishop, C.M., 2006. Pattern Recognition and Machine Learning, Information science and statistics. New York: Springer.
Cartwright, R.L., 1968. Some Remarks on Essentialism. The Journal of Philosophy [Online], 65(20), pp.615–626. https://doi.org/10.2307/2024315.
Cohen, M.F., 1968. Wittgenstein’s anti-essentialism. Australasian Journal of Philosophy [Online], 46(3), pp.210–224. https://doi.org/10.1080/00048406812341181.
Descartes, R., 1641. Renati Des-Cartes Meditationes de prima philosophia, in qua Dei existentia et animae immortalitas demonstratur. [Online]. Paris: Michael Soly. Available at: <https://gallica.bnf.fr/ark:/12148/btv1b86002964> [visited on 25 August 2021].
Descartes, R., 1991. Meditations on First Philosophy. The Philosophical Writings of Descartes, vol. 2 (J. Cottingham, R. Stoothoff and D. Murdoch, Trans.). Cambridge: Cambridge University Press, pp.1–63.
Duin, R.P., 2015. The dissimilarity representation for finding universals from particulars by an anti-essentialist approach. Pattern Recognition Letters [Online], 64(C), pp.37–43. https://doi.org/10.1016/j.patrec.2015.04.015.
Gelman, S., 2004. Psychological essentialism in children. Trends in Cognitive Sciences [Online], 8(9), pp.404–409. https://doi.org/10.1016/j.tics.2004.07.001.
Gelman, S.A., 2005. Essentialism in Everyday Thought. Available at: <https://www.apa.org/science/about/psa/2005/05/gelman> [visited on 12 January 2023].
Gibbs, C., 2018. Causal essentialism and the identity of indiscernibles. Philosophical Studies [Online], 175(9), pp.2331–2351. https://doi.org/10.1007/s11098-017-0961-y.
Kripke, S.A., 1972. Naming and Necessity. In: D. Davidson and G. Harman, eds. Semantics of Natural Language [Online], Synthese Library. Dordrecht: Springer Netherlands, pp.253–355. https://doi.org/10.1007/978-94-010-2557-7_9.
Krzanowski, R. and Polak, P., 2022a. Ontology and AI Paradigms. Proceedings [Online], 81(1), p.119. https://doi.org/10.3390/proceedings2022081119.
Krzanowski, R. and Polak, P., 2022b. The Meta-Ontology of AI systems with Human-Level Intelligence. Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce), (73), pp.23–24.
Lapuschkin, S. et al., 2019. Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications [Online], 10(1), p.1096. https://doi.org/10.1038/s41467-019-08987-4.
Liu, H. and Motoda, H., 1998. Feature Selection for Knowledge Discovery and Data Mining. Norwell, MA: Kluwer Academic Publishers.
Mackie, P., 2006. How Things Might Have Been: Individuals, Kinds, and Essential Properties [Online]. 1st ed. Oxford: Oxford University Press. https://doi.org/10.1093/0199272204.001.0001.
Marcus, R.B., 1993. Modalities: Philosophical Essays [Online]. New York: Oxford University Press. Available at: <http://catdir.loc.gov/catdir/enhancements/fy0638/91048105-t.html> [visited on 12 January 2023].
Matthews, G.B., 1990. Aristotelian Essentialism. Philosophy and Phenomenological Research [Online], 50, pp.251–262. https://doi.org/10.2307/2108042.
Mohri, M., Rostamizadeh, A. and Talwalkar, A., 2018. Foundations of Machine Learning [Online]. 2nd ed., Adaptive computation and machine learning. Cambridge, MA: The MIT Press. Available at: <https://cs.nyu.edu/~mohri/mlbook/> [visited on 13 January 2023].
Palmer, F.R., 1981. Semantics [Online]. 2nd ed. Cambridge: Cambridge University Press. Available at: <http://archive.org/details/semantics00palm> [visited on 13 January 2023].
Pelillo, M., 2013. Introduction: The SIMBAD Project. In: M. Pelillo, ed. Similarity-Based Pattern Analysis and Recognition [Online], Advances in Computer Vision and Pattern Recognition. London; Heidelberg; New York; Dordrecht: Springer, pp.1–10. https://doi.org/10.1007/978-1-4471-5628-4_1.
Pelillo, M. and Scantamburlo, T., 2013. How Mature Is the Field of Machine Learning? In: D. Hutchison et al., eds. AI*IA 2013: Advances in Artificial Intelligence [Online]. Vol. 8249. Cham: Springer International Publishing, pp.121–132. https://doi.org/10.1007/978-3-319-03524-6_11.
Pottier, B., 1964. Vers une sémantique moderne. Strasbourg: Klincksieck.
Robertson Ishii, T. and Atkins, P., 2020. Essential vs. Accidental Properties. In: E.N. Zalta, ed. The Stanford Encyclopedia of Philosophy [Online]. Winter 2020. Stanford, CA: Metaphysics Research Lab, Stanford University. Available at: <https://plato.stanford.edu/archives/win2020/entries/essential-accidental/>.
Rosch, E.H., 1973. Natural categories. Cognitive Psychology [Online], 4(3), pp.328–350. https://doi.org/10.1016/0010-0285(73)90017-0.
Skansi, S., 2018. Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence [Online], Undergraduate Topics in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-73004-2.
Tunç, B., 2015. Semantics of object representation in machine learning. Pattern Recognition Letters [Online], 64(15), pp.30–36. https://doi.org/10.1016/j.patrec.2015.03.016.
Watanabe, S., 1985. Pattern Recognition: Human and Mechanical. New York: John Wiley & Sons, Inc.
Zhang, M., 2015. Google Photos Tags Two African-Americans As Gorillas Through Facial Recognition Software. Available at: <https://www.forbes.com/sites/mzhang/2015/07/01/google-photos-tags-two-african-americans-as-gorillas-through-facial-recognition-software/> [visited on 13 January 2023].
Zheng, A. and Casari, A., 2018. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. 1st ed. Beijing; Boston; Farnham; Sebastopol; Tokyo: O’Reilly.